Though public overhead imagery datasets explore geographic and sensor homogeneity [1]}, [2]}, [3]}, [4]}, [5]}, they generally comprise a single view of the imaged location(s) taken nearly directly overhead (“at nadir"). Nadir imagery is not representative of collections during disaster response or other urgent situations: for example, the first public high-resolution cloud-free image of San Juan, Puerto Rico following Hurricane Maria was taken at \(51.9^\circ \)  “off-nadir", i.e., a \(51.9^\circ \)  angle between the nadir point directly underneath the satellite and the center of the imaged scene [6]}. The disparity between looks in public training data and relevant use cases hinders development of models applicable to real-world problems. More generally, satellite and drone images rarely capture identical looks at objects in different contexts, or even when repeatedly imaging the same geography. Furthermore, no existing datasets or metrics permit assessment of model robustness to different looks, prohibiting evaluation of performance. These limitations extend to tasks outside of the geospatial domain: for example, convolutional neural nets perform inconsistently in many natural scene video frame classification tasks despite minimal pixel-level variation [7]}, and Xiao et al. showed that spatial transformation of images, effectively altering view, represents an effective adversarial attack against computer vision models [8]}. Addressing generalization across views both within and outside of the geospatial domain requires two advancements: 1. A large multi-view dataset with diversity in land usage, population density, and views, and 2. A metric to assess model generalization.
